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CROSSING FRAMEWORK A Dynamic Infrastructure to Develop Knowledge-based Recommenders in Cross Domains

AZAK, Mustafa
Birtürk, Ayşe Nur
We propose a dynamic framework that differs from the previous works as it focuses on the easy development of knowledge-based recommenders and it proposes an intensive cross domain capability with the help of domain knowledge. The framework has a generic and flexible structure that data models and user interfaces are generated based on ontologies. New recommendation domains can be integrated to the framework easily in order to improve recommendation diversity. We accomplish the cross-domain recommendation via an abstraction in domain features if the direct matching of the domain features is not possible when the domains are not very close to each other.